Map Library Research Articles

Gaussian process regression on multiple drivers and attributes for rapid prediction of maximum flood inundation extent and depth

Traditional high-resolution flood models are too slow for real-time predictions. The most common industry practice is to use a lookup table or interpolation algorithm to derive flood extents from a pre-generated library of flood maps. For the library interpolation approach to be effective, the input flood data need to closely match those in the map library. To effectively emulate complex and dynamic flood behaviour, the interpolation approach should be able to account for multiple flood drivers (such as rivers, tributaries and tides) and attributes (such as shape and timing of the hydrographs). However, a simple extension of existing interpolation algorithms would make them overly complex and need a very large map library to deal with these drivers and attributes. To address this challenge, this study investigates the capability of a Gaussian Process (GP) modelling approach to accommodate the complex influence of multiple flood drivers and attributes, and thus to provide robust, accurate and fast predictions. By training the GP model, it learns the underlying relationships between flood depths and multiple flood drivers and attributes. Model accuracy and speed in predicting maximum flood extents and depths are examined. In a case study of a floodplain in Port Fairy, Australia, the GP model is found to generally outperform the library interpolation approach in accuracy, particularly for complex floods, while being similar efficient. The GP model is a promising approach for real-time flood predictions.

Experiences using maps and story maps in the BIO-MAPS project (Online Map Library of European writers and poets)

Experiences using maps and story maps in the BIO-MAPS project (Online Map Library of European writers and poets)

Australian property market explorer – a front-end native tool for visualizing property sales data

Online interactive 3D visualizations can enhance understanding of the built environment, including non-physical aspects such as property value and price. With access to large-scale open datasets, a key challenge in building such interactive 3D mapping interfaces is the efficiency of visualization methods. Modern GPU-accelerated front-end mapping libraries present a solution to this problem. This research proposes a ‘front-end’ native approach to create a web-based visual analytics framework for large scale spatiotemporal data. The Australian Property Market Explorer exemplifies this framework by visualizing a large dataset of property sales in New South Wales, Australia. This tool allows users to explore and analyse property market patterns at various geographic scales. The proposed framework offers advantages over traditional methods such as scalability and reliability while promising use-cases such as aiding planners in infrastructure and transportation decisions and assisting property valuation professionals in analysing property price trends.

Calibration-free parallel transmission of the cervical, thoracic, and lumbar spinal cord at 7T.

To address the limitations of spinal cord imaging at ultra-high field (UHF) due to time-consuming parallel transmit (pTx) adjustments. This study introduces calibration-free offline computed universal shim modes that can be applied seamlessly for different pTx RF coils and spinal cord target regions, substantially enhancing spinal cord imaging efficiency at UHF. A library of channel-wise relative maps for the cervical spinal cord (six datasets) and thoracic and lumbar spinal cord (nine datasets) was constructed to optimize transmit homogeneity and efficiency for these regions. A tailored B0 shim was optimized for the cervical spine to enhance spatial magnetic field homogeneity further. The performance of the universal shims was validated using absolute saturation based mapping and high-resolution 2D and 3D multi-echo gradient-recalled echo (GRE) data to assess the image quality. The proposed universal shims demonstrated a 50% improvement in efficiency compared to the default (zero phase) shim mode. homogeneity was also improved by 20%. The optimized universal shims achieved performance comparable to subject-specific pTx adjustments, while eliminating the need for lengthy pTx calibration times, saving about 10 min per experiment. The development of universal shims represents a significant advance by eliminating time-consuming subject-specific pTx adjustments. This approach is expected to make UHF spinal cord imaging more accessible and user-friendly, particularly for non-pTx experts.

Abstract NG05: Epitope editing enables targeted immunotherapy of acute myeloid leukemia

Abstract NG05: Epitope editing enables targeted immunotherapy of acute myeloid leukemia

Tracking information in the field: An assessment of the information needs and services of a field station library

This project examines field station libraries, an undescribed type of library associated with terrestrial and marine field stations. Field stations typically support ecology, geology, and related disciplines with housing, laboratories, and research facilities, which may include various levels of library support. In summer 2022, researchers, instructors, and staff at the University of Michigan Biological Station were interviewed about the library resources and services they use while working and teaching at the field station. Library resources were used throughout teaching and research activities, with one-shot instruction and in-person reference support as the primary contact points between the library and the field station community. The field station library was further defined by the community's need for GIS assistance, map librarianship, research data management, and prior on-site research stored in the institutional repository. Course-based research and place-based research significantly shaped the community's information needs when working at the field station. When characterizing the information and library service needs of the field station community, these services should be placed at the core. While this directly applies to terrestrial field stations, the findings may be applicable to ecology and geology librarianship more generally.

Towards reference model for addressing the cultural snapshot phenomenon in cultural mapping libraries

Towards reference model for addressing the cultural snapshot phenomenon in cultural mapping libraries

Streamlining logistics flows with lean tools using TX Plant Simulation software support

A critical factor in the manufacturing industry is waste and losses in individual operations and processes. The expansion of elements of lean production, such as the pull system with the interconnection of key technologies, brings new elements of production planning and control. Thanks to new technologies, many more possibilities exist to uncover potential bottlenecks. Simulations and modelling bring new possibilities for experimentation in the virtual environment of prepared systems or devices without directly disturbing the functioning system. The presented article deals with the issue of applying lean approaches in finding optimal solutions within logistics flows. The aim of the article is to define and test the potential for streamlining logistics flows with TX Plant Simulation software support. The research and empirical part of the study was solved based on a rigorous analysis of the initial situation in a specific company for the selected product group, as the Value Stream Mapping method was used for the analysis. A simulation model of a real line was created in the TX Plant Simulation software with the help of the Value Stream Mapping library for value creation, which is part of the software. The goal was to find out possible waste within the logistics flow, and its causes and propose the necessary steps to eliminate the waste.

Researching and applying open-source WebGIS technology to build national-standard vector geographic database management software

Geographical database management is a crucial component of spatial data infrastructures (SDIs) and is used by government organizations in provinces, regions, and countries. It plays an important role, especially during the 4.0 technology revolution and digital transformation to build e-government and digital government. A complete and scientific management of geographical databases is the most crucial core foundation of a system. The article applies open-source WebGIS technologies such as Geoserver map server, PostGIS data management software, Python programming language, Vue, and Openlayers map library to build spatial data management software. Spatial data is mainly in vector form, built according to national geographic database standards (QCVN 42: 2020/BTNMT). The system has main functional groups such as user account authorization and user groups; and geographic database management, including seven groups: borders, geodesy, population, topography, traffic, surface cover, and hydrology. In addition, the administration of some thematic classes such as drafting plans, managing equipment and units, etc., are performed. The software system is built in a service-oriented manner, serving as the core foundation to facilitate the development of many geo-portal applications.

A multi-system comparison of forecast flooding extent using a scale-selective approach

Abstract Fluvial flood forecasting systems increasingly couple river discharge to a flood map library or a real-time hydrodynamic model to provide forecast flood maps to humanitarian agencies. The forecast flood maps can be linked to potential impacts to inform forecast-based financing schemes. We investigate a new application of scale-selective verification by evaluating three flood forecasting systems. Two simulation library systems, Flood Foresight (30 m) and GloFAS Rapid Flood Mapping (1,000 m) and one hydrodynamically modelled system, the Bangladesh Flood Forecasting and Warning Centre (FFWC) Super Model (300 m), all made predictions of flooding extent at different spatial scales (grid lengths, in brackets) for the Jamuna River flood, Bangladesh, July 2020. The flood maps are validated against synthetic-aperture-radar-derived observations of flooding using a scale-selective approach that can compare directly across different spatial scales. At short forecast lead times, the Super Model outperforms the other systems. Near to the Bangladesh border, the trans-boundary benefits of the two global systems are evident. We find that scale-selective methods can quantify the skill of systems operating at different spatial scales so that the benefits and limitations can be evaluated. Multi-system comparison of flood maps is important for improving impact-based forecasts and ensuring funds and response activities are appropriately targeted.

Hungarian writers and poets in the BIO-MAPS Biographical Map Library

Hungarian writers and poets in the BIO-MAPS Biographical Map Library

Automatic Labels in Leaflet

Abstract. Automatic labelling is a lacking feature in the popular client-side web mapping library, Leaflet. Although there exist various workarounds and substitutes, most of these do not fulfil the basic cartographic rules, e.g. avoiding text overlaps.This paper discusses the various workarounds addressing this problem, and introduces the author’s solution, which extends Leaflet’s GeoJSON class with automatic labelling functionality. The new class displays labels for point, line or polygon features. Labels can be placed either on right or left side of points or automatically for better fitting. Markers for points are only displayed if their labels can be drawn without overlaps. Label text is specified either by property field name or a user-specified function. It is also possible to specify priority order of features (higher priority labels are drawn first) and to customize label styles.

The ricebean genome provides insight into Vigna genome evolution and facilitates genetic enhancement.

The ricebean genome provides insight into Vigna genome evolution and facilitates genetic enhancement.

OCTREE-BASED APPROACH FOR REAL-TIME 3D INDOOR MAPPING USING RGB-D VIDEO DATA

Abstract. 3D indoor mapping is becoming increasingly critical for a variety of applications such as path planning and navigation for robots. In recent years, there is a growing interest in how low-cost sensors, such as monocular or depth cameras, can be used for 3D mapping. In our paper, we present an octree-based approach for real-time 3D indoor mapping using a handheld RGB depth camera. One benefit of the generated octree map is that it requires less storage and computational resources than point cloud models. Moreover, it explicitly represents free space and unmapped areas, which are essential for the robot's navigation tasks. In this work, on the basis of the ORB-SLAM3 system (Campos et al., 2021), we developed an octree mapping system, which directly calls the keyframes and estimated poses provided by ORB-SLAM3 algorithms. Furthermore, we used point cloud library (PCL) for the dense point cloud mapping and then OctoMap for the point cloud to octree map conversion. Finally, we implemented an efficient probabilistic 3D mapping in the robot operating system (ROS) environment. We used the TUM RGB-D dataset to evaluate the estimated trajectories of the camera. The evaluation shows an average translational RMSE of 5.9 cm on the TUM RGB-D dataset. Besides, we also compared the ground truth point clouds and our generated point clouds. The result shows the mean cloud-to-cloud distance in the corridor scene is about 6 cm. All the evaluation results show our proposed approach is a promising solution for advanced indoor voxel mapping and robotic navigation systems.

Mapping floods from remote sensing data and quantifying the effects of surface obstruction by clouds and vegetation

Floods are one of the most devastating natural calamities affecting millions of people and causing damage all around the globe. Flood models and remote sensing imagery are often used to predict and understand flooding. An increasing number of earth observation satellites are producing data at a rate that far outpaces our ability to manually extract meaningful information from it, motivating a surge in research on automatic feature detection in satellite imagery using machine learning and deep learning algorithms to automate flood mapping so that information from large streams of data can be extracted in near-real time and used for disaster response at landscape scale. The development of such an algorithm is predicated on exposure to training datasets that are representative of the full range of diversity in the spatial and spectral signature of surface water as it is sampled by space-based instruments. To address these needs, we developed a semantically labeled dataset of high-resolution multispectral imagery (Maxar WorldView 2/3) strategically sampled to be representative of North American surface water variability along five spatiotemporal strata: latitude, topographic complexity, land use, and day of year. This dataset was utilized to train a convolutional neural network (CNN) to automatically detect inundation extents using the Deep Earth Learning, Tools, and Analysis (DELTA) framework, an open source TensorFlow/Keras interpreter for satellite imagery. Our research objective was to demonstrate the out-of-sample accuracy of our trained CNN at landscape scale. The model performed well, with 98% precision and 94% recall for the water class during validation. We then evaluated the accuracy of our satellite-derived flood maps from trained machine learning model against a hydraulic model. For this, we compared predicted inundation extents against the USGS Flood Inundation Mapping (FIM) Program's flood map library at 17 different locations, where the FIM library provides flood inundation extents based on hydraulic models built for river reaches and corresponding to stage measurements at a nearby USGS gaging site. Compared to the hydraulic model, we estimated the underprediction of flood inundation by optical remote sensing data in our areas of interest to be 62%. We used land use data from National Land Cover Database (NLCD) and cloud masks to estimate that 79% of underprediction was due to these obstructions, with 74% belonging to vegetation, 9% to clouds, and 4% to both. A significant amount of inundation is missed when only optical remote sensing data is considered, and we suggest the use of flood models along with remote sensing data for getting the most realistic flood inundation extents.

Mapping Library Users’ Loyalty through LibQUAL +: Mediating Role of Users’ Satisfaction

The purpose of this study was to investigate the gap between library users’ expected and perceived library services and to examine the role of library services in developing user satisfaction and loyalty. A quantitative research design using a questionnaire was used to investigate the research objectives. Undergraduate students were selected as the population from the Information Technology University. The questionnaire was adopted from previous literature. It was pilot tested with 50 undergraduate students and found reliable. Data was collected from 304 respondents and were analyzed using SPSS (v.20) software. The study found that library users expected a high level of service in three categories, i.e., Affect of Service, Information Control, and Library as a Place, as all mean scores were higher than 7 when rated against a scale from 1 to 9. The participants reported that they were likely to consult the library and recommend it to others. The structural equation model confirmed that better services will increase users’ satisfaction with the library resources and services and will lead to winning users’ loyalty. The findings from the study provided useful insight into the determinants of user satisfaction and loyalty and could be useful for other academic libraries seeking ways to increase user loyalty and user satisfaction with library resources and services.

Academic Map Library Weeding

While maps remain an important part of library work, there has been a dearth of scholarly discussion on weeding maps. Some authors have offered suggestions in particular contexts – notably in public libraries and U.S. depository libraries. This leaves significant room for further discussion for those outside of the U.S. context, and those with more general map libraries. Following a literature review and contextual information situating the author's involvement in the topic, a discussion of methods and appropriate considerations are offered for weeding map collections. These guidelines are offered in point form in the conclusion for ease of referral.

Creation of a GIS project of the results of testing technologies
 to reduce the accumulation of radionuclides in agricultural products

The article presents the results of the use of drones and GIS technologies during pilot testing of new types of agro-ameliorants and humic preparations to assess the effectiveness of reducing the accumulation of radionuclides in agricultural products grown on radioactively contaminated agricultural land in the Bryansk region. In the course of the work, an assessment of experimental farms was carried out to select the optimal sites for testing, as well as their digitization using an unmanned aerial vehicle equipped with a two-piece geodetic receiver to determine the exact geographical coordinates. Subsequently, with the help of a drone, fertilizer application was monitored, and the germination of agricultural plants was assessed after the application of agromeliorants.
 Based on the received materials, a library of electronic maps of the territories where the work was carried out was created in the ArcGis software environment. They included 4 farms of the Novozybkov district and 2 farms of the Krasnogorsk district. The library of electronic maps included cartographic information on the types of land use, surface contamination of soils with 137Cs, types and granulometric composition of soils, their agrochemical parameters, germination, yield, reduction ratio of 137Cs inflow into crop and forage production, as well as on the boundaries of elementary experimental plots with the designation type of fertilizer applied.

BIO-MAPS: Biographical map library of writers and poets of three European countries

BIO-MAPS: Biographical map library of writers and poets of three European countries

Here Today, Here Tomorrow: A Case Study of Reviewing a Map Library with Eyes toward the Future

In 2021, the University of New Mexico’s University Libraries conducted an evaluation of its Map and Geographic Information Center (MAGIC). MAGIC is the largest public map library in New Mexico, though usage has dropped over the last ten years. The author led a team of six people investigating MAGIC, reviewing documentation and conducting interviews to understand its usage, collection, services, and personnel in order to provide options for the University Libraries’ administration. This case study covers the 10-month review and the options provided, which range from closure to transformation.